Power amplifier voltage adjusting method and system based on machine learning

A technology of voltage adjustment and machine learning, applied in the field of communication, to achieve the effect of real-time dynamic adjustment of power consumption and energy consumption, and optimization of power consumption

Active Publication Date: 2022-01-18
GUANGDONG COMM & NETWORKS INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the process of reducing the energy consumption of the base station, there is still room for further reducing the energy consumption of the base station software.

Method used

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  • Power amplifier voltage adjusting method and system based on machine learning
  • Power amplifier voltage adjusting method and system based on machine learning
  • Power amplifier voltage adjusting method and system based on machine learning

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Experimental program
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Embodiment 1

[0024] see figure 1 , figure 1 It is a schematic flowchart of a machine learning-based power amplifier voltage adjustment method disclosed in an embodiment of the present invention. like figure 1 As shown, the power amplifier voltage adjustment method based on machine learning may include the following operations:

[0025] 101. Construct a machine learning model based on voltage parameter training.

[0026] In the present invention, the applicable power amplifier circuit structure can be implemented as a Doherty+DPD architecture to achieve high efficiency of the power amplifier components, wherein, DPD is a band-limited digital pre-distortion, and the main implementation method is to input the signals of the power amplifier components and The output signal of the power amplifier part is sampled, and an error algorithm is performed, so that a signal opposite to the distortion of the power amplifier is added to the input port of the power amplifier to offset the distortion of...

Embodiment 2

[0036] see image 3 , image 3 It is a schematic diagram of a power amplifier voltage adjustment system based on machine learning disclosed in an embodiment of the present invention. like image 3 As shown, the power amplifier voltage adjustment system based on machine learning includes:

[0037] Machine learning model 1 and power amplifier adjustment module 2. Wherein, the machine learning model 1 formed based on voltage parameter training is used to obtain the current traffic volume, and predict the current traffic volume to generate a prediction result. The power amplifier adjustment module 2 is configured to automatically adjust the power amplifier voltage based on the power amplifier adjustment algorithm and prediction results, wherein the prediction results include power alignment results, time delay alignment results, and peak clipping coefficient update results.

[0038] Wherein, the machine learning model 1 includes: a parameter acquisition module 11, configured t...

Embodiment 3

[0043] see Figure 4 , Figure 4 It is a schematic structural diagram of a power amplifier voltage adjustment device based on machine learning disclosed in an embodiment of the present invention. in, Figure 4 The described device for adjusting the voltage of a power amplifier based on machine learning can be applied to a power amplifier voltage system, and the embodiment of the present invention does not limit the application system of the device for adjusting the voltage of a power amplifier based on machine learning. like Figure 4 As shown, the device may include:

[0044] A memory 601 storing executable program codes;

[0045] a processor 602 coupled to the memory 601;

[0046] The processor 602 invokes the executable program code stored in the memory 601 to execute the method for adjusting the power amplifier voltage based on machine learning described in the first embodiment.

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Abstract

The invention discloses a power amplifier voltage adjusting method based on machine learning. The method comprises the following steps of constructing a machine learning model formed based on voltage parameter training, obtaining the current telephone traffic, and predicting the current telephone traffic based on a machine learning model to generate a prediction result, automatically adjusting the power amplifier voltage based on a preset power amplifier adjustment algorithm and prediction results, wherein the prediction results comprise a power alignment result, a time delay alignment result and a peak clipping coefficient updating result. Therefore, according to the method disclosed by the invention, the power amplifier voltage can be dynamically adjusted to improve the overall power amplifier efficiency of the circuit by predicting the change of the telephone traffic under the condition that a turn-off mode is not needed, so that the energy consumption of the power amplifier during working is greatly reduced.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a machine learning-based power amplifier voltage adjustment method and system. Background technique [0002] The existing 5G base station software energy-saving direction is mainly "shutdown", such as symbol shutdown, time slot shutdown, carrier shutdown, channel shutdown, base station deep sleep, etc. In order to avoid the impact of "shutdown" behavior on the operation of 5G base stations, the existing energy-saving methods generally find a corresponding relationship in advance and store it in the storage system of the control unit. Before the base station leaves the factory, a static lookup table is established through empirical formulas to find Correlation relationship to obtain power amplifier voltage parameters, such as power control level-power amplifier voltage correspondence, carrier number-clipping threshold-power amplifier voltage correspondence, average ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03G3/30G06N20/00H04W52/02H04W52/18
CPCH03G3/3042G06N20/00H04W52/0206H04W52/18Y02D30/70
Inventor 刘畅远闫书保陈传友
Owner GUANGDONG COMM & NETWORKS INST
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